Predicting phenotypes of a living being in real-time

Abstract

Described herein are technologies pertaining to predicting whether a living being, such as a human being, an animal, or a plant, has a phenotype or set of phenotypes in real-time or near real-time. A filter set of genetic markers are determined heuristically, by first univariately computing scores for respective genetic markers that are indicative of their predictive ability with respect to the phenotype or the set of phenotypes. Thereafter, during training, the filter set is initially selected and thereafter expanded based upon the scores, until predictive accuracy for the phenotype or set of phenotypes reaches a threshold or is optimized. The filter set, which includes a relatively small number of genetic markers, is subsequently employed for real-time or near-real time phenotype prediction.

1 . A method executed by a processor, the method comprising:
 receiving a data packet that comprises a plurality of values that correspond to a respective plurality of genetic markers for a living being, the plurality of genetic markers comprising at least one of single nucleotide polymorphisms, copy number variations, or epigenetic markers;   filtering from the plurality of values a subset of values for a respective subset of pre-defined genetic markers of the living being, a number of values in the subset of values being less than a number of values in the data packet, and wherein identities of genetic markers in the subset of genetic markers are learned in a pre-processing stage to optimize phenotype prediction;   predicting at least one phenotype of the living being based at least in part upon the subset of values for the respective subset of pre-defined genetic markers; and   outputting graphical data to a display screen of a computing device based at least in part upon the predicting of the at least one phenotype of the living being.   
     
     
         2 . The method of  claim 1 , wherein predicting the at least one phenotype of the living being is further based at least in part upon values of non-genetic features of the living being. 
     
     
         3 . The method of  claim 1 , wherein the living being is a human being. 
     
     
         4 . The method of  claim 1 , wherein the living being is a domesticated animal. 
     
     
         5 . The method of  claim 1 , wherein the living being is a plant. 
     
     
         6 . The method of  claim 1 , wherein predicting the at least one phenotype of the living being comprises executing a linear mixed model algorithm over at least the subset of values. 
     
     
         7 . The method of  claim 1 , wherein the number of values in the subset of values is less than five percent of the number of values in the plurality of values. 
     
     
         8 . The method of  claim 1 , wherein the number of values in the subset of values is less than ten thousand values. 
     
     
         9 . The method of  claim 1 , wherein the number of values in the subset of values is less than five thousand values. 
     
     
         10 . The method of  claim 1 , wherein the number of values in the subset of values is less than one thousand values. 
     
     
         11 . The method of  claim 1 , wherein the at least one phenotype pertains to at least one of:
 whether the living being will have an undesirable reaction upon consuming a pharmaceutical drug;   whether the living being will have a desirable reaction upon consuming a pharmaceutical drug;   whether the living being will get a particular disease; or   whether a prescribed dosage of a pharmaceutical drug is appropriate for the living being.   
     
     
         12 . A system that facilitates predicting that a living being has a specified phenotype, the system comprising:
 a processor; and   a memory in operable communication with the processor, the memory comprising a plurality of components that are executed by the processor, the plurality of components comprising:
 a filter component that receives:
 a plurality of values for a respective plurality of genetic markers for a living being; and 
 identities of genetic markers in a learned set of genetic markers, the genetic markers in the learned set of genetic markers having been learned in a training phase to optimize prediction for the specified phenotype; 
 wherein the filter component outputs a subset of values from the plurality of values, the subset of values being for genetic markers in the learned set of genetic markers, and 
 
   a predictor component that receives the subset of values and outputs a prediction as to whether the living being has the specified phenotype based at least in part upon the subset of values.   
     
     
         13 . The system of  claim 12 , wherein the filter component populates a similarity matrix with the subset of values, and wherein the predictor component executes a linear mixed model algorithm over the similarity matrix to output the prediction. 
     
     
         14 . The system of  claim 12 , wherein a first number of values in the subset of values is less than a second number of values in the plurality of values. 
     
     
         15 . The system of  claim 12 , wherein the first number of values is less than ten thousand. 
     
     
         16 . The system of  claim 12 , wherein the first number of values is less than five thousand. 
     
     
         17 . The system of  claim 12 , wherein the genetic markers are single nucleotide polymorphisms. 
     
     
         18 . The system of  claim 12 , wherein the living being is a human being or a domesticated animal. 
     
     
         19 . The system of  claim 12  comprised by a mobile computing device. 
     
     
         20 . A computer-readable medium comprising instructions that, when executed by a processor, cause the processor to perform acts comprising:
 receiving identities of genetic markers in a learned set of genetic markers, the genetic markers in the learned set of genetic markers learned to optimize predictive accuracy for a specified phenotype in human beings;   receiving, for a human being, values for respective genetic markers of the human being;   extracting a subset of values from the values for the respective genetic markers for the human being, the subset of values being for the genetic markers in the learned set of genetic markers, and a number of values in the subset of values being less than ten thousand;   populating a similarity matrix with the subset of values; and   executing a linear mixed model algorithm over the similarity matrix to output data that is indicative of whether the human being has the specified phenotype.